R Technology Workshop

R is the most popular free software environment for statistical computing and graphics. ggplot2 is a data visualization package for R that can be used to produce publication-quality graphics. This workshop is designed to introduce you to R and ggplot as well as RStudio, KnitR, Slidify, and Shiny.
R is a central piece of the Big Data Analytics Revolution, for example, see http://opensource.com/business/14/7/interview-david-smith-revolution-analytics for an article entitled “Big data influencer on how R is paving the way”

This is how my RStudio is configured:

sessionInfo()
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin10.8.0 (64-bit)
## Running under: OS X 10.8.5 (Mountain Lion)
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] magrittr_1.5    tools_3.2.1     htmltools_0.2.6 yaml_2.1.13    
##  [5] stringi_0.5-5   rmarkdown_0.8   knitr_1.11      stringr_1.0.0  
##  [9] digest_0.6.8    evaluate_0.7.2

You also need to install LaTeX if you want to generate PDF files from KnitR.

http://latex-project.org/ftp.html

Getting Started - Clone the RWorkshop GiT Repository:

Use a GUI tool like SourceTree to clone the repository or execute the following commands in a terminal window:

Phils-MacBook-Pro:Mine pcannata$ pwd
/Users/pcannata
Phils-MacBook-Pro:~ pcannata$ git clone https://github.com/pcannata/DataVisualization.git
Cloning into ‘DataVisualization’… remote: Counting objects: 74, done. remote: Compressing objects: 100% (60/60), done. remote: Total 74 (delta 6), reused 67 (delta 4) Unpacking objects: 100% (74/74), done. Checking connectivity… done.
Phils-MacBook-Pro:~ pcannata$ ls -a DataVisualization/
. .. .git README.md RWorkshop

Getting Started - Create a New RStudio Project for the code in the cloned repository:

Getting Started - Create a .Rprofile file to load libraries when the project is started:

Create an new file text named .Rprofile.

Put the following into .Rprofile
require(“ggplot2”) require(“ggthemes”) require(“gplots”) require(“grid”) require(“RCurl”) require(“reshape2”) require(“rstudio”) require(“tableplot”) require(“tidyr”) require(“dplyr”) require(“jsonlite”) require(“extrafont”) require(“lubridate”)

Be sure to put a newline after the last require statement.

00 High Level Overview - Creating an Excel-like Chart in R - see the 00 Overview Folder in the DrCannata/Rworkshop Repository

This is something that is easily done in Excel:

How would you do the same thing in R?

source("../00 Overview/Overview.R", echo = TRUE)
## 
## > x <- c(1, 2, 3, 4, 5)
## 
## > y <- 3 * x
## 
## > y1 <- 2^x
## 
## > x
## [1] 1 2 3 4 5
## 
## > y
## [1]  3  6  9 12 15
## 
## > y1
## [1]  2  4  8 16 32
## 
## > df <- data.frame(x, y, y1)
## 
## > df
##   x  y y1
## 1 1  3  2
## 2 2  6  4
## 3 3  9  8
## 4 4 12 16
## 5 5 15 32
## 
## > require(reshape2)
## Loading required package: reshape2
## 
## > mdf <- melt(df, id.vars = "x", measure.vars = c("y", 
## +     "y1"))
## 
## > mdf
##    x variable value
## 1  1        y     3
## 2  2        y     6
## 3  3        y     9
## 4  4        y    12
## 5  5        y    15
## 6  1       y1     2
## 7  2       y1     4
## 8  3       y1     8
## 9  4       y1    16
## 10 5       y1    32
## 
## > require(ggplot2)
## Loading required package: ggplot2
## 
## > ggplot(mdf, aes(x = x, y = value, color = variable)) + 
## +     geom_line()

See also http://cran.r-project.org/doc/manuals/r-devel/R-lang.html, http://www.r-tutor.com/r-introduction, and http://www.cookbook-r.com/

01 R Dataframes - see the 02 R Dataframes Folder in the DrCannata/Rworkshop Repository

A data frame is used for storing data tables. It is a list of vectors of equal length. For example, the following variable df is a data frame containing three vectors n, s, b.

n = c(2, 3, 5) 
s = c("aa", "bb", "cc") 
b = c(TRUE, FALSE, TRUE) 
df = data.frame(n, s, b)       # df is a data frame
head(df)
##   n  s     b
## 1 2 aa  TRUE
## 2 3 bb FALSE
## 3 5 cc  TRUE

Dataframes can be loaded from databases, CSVs, Excel, etc.. Loading dataframes from an Oracle database will be discussed later in this Workshop.

See also http://www.r-tutor.com/r-introduction/data-frame

Many R packages come with demo dataframes. The ggplot package comes with a demo dataframe called diamonds which we will use for this workshop.

source("../01 R Dataframes/Dataframes.R", echo = TRUE)
## 
## > require("ggplot2")
## 
## > "Displaying the top few rows of a dataframe:"
## [1] "Displaying the top few rows of a dataframe:"
## 
## > head(diamonds)
##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48
## 
## > "Summary of each variable in the dataframe."
## [1] "Summary of each variable in the dataframe."
## 
## > names(diamonds)
##  [1] "carat"   "cut"     "color"   "clarity" "depth"   "table"   "price"  
##  [8] "x"       "y"       "z"      
## 
## > `?`(diamonds)
## 
## > summary(diamonds)
##      carat               cut        color        clarity     
##  Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065  
##  1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258  
##  Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194  
##  Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171  
##  3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066  
##  Max.   :5.0100                     I: 5422   VVS1   : 3655  
##                                     J: 2808   (Other): 2531  
##      depth           table           price             x         
##  Min.   :43.00   Min.   :43.00   Min.   :  326   Min.   : 0.000  
##  1st Qu.:61.00   1st Qu.:56.00   1st Qu.:  950   1st Qu.: 4.710  
##  Median :61.80   Median :57.00   Median : 2401   Median : 5.700  
##  Mean   :61.75   Mean   :57.46   Mean   : 3933   Mean   : 5.731  
##  3rd Qu.:62.50   3rd Qu.:59.00   3rd Qu.: 5324   3rd Qu.: 6.540  
##  Max.   :79.00   Max.   :95.00   Max.   :18823   Max.   :10.740  
##                                                                  
##        y                z         
##  Min.   : 0.000   Min.   : 0.000  
##  1st Qu.: 4.720   1st Qu.: 2.910  
##  Median : 5.710   Median : 3.530  
##  Mean   : 5.735   Mean   : 3.539  
##  3rd Qu.: 6.540   3rd Qu.: 4.040  
##  Max.   :58.900   Max.   :31.800  
##                                   
## 
## > "Selecting a subset of columns from a dataframe:"
## [1] "Selecting a subset of columns from a dataframe:"
## 
## > head(subset(diamonds, select = c(carat, cut)))
##   carat       cut
## 1  0.23     Ideal
## 2  0.21   Premium
## 3  0.23      Good
## 4  0.29   Premium
## 5  0.31      Good
## 6  0.24 Very Good
## 
## > "Selecting a subset of rows from a dataframe:"
## [1] "Selecting a subset of rows from a dataframe:"
## 
## > head(subset(diamonds, cut == "Ideal" & price > 5000))
##       carat   cut color clarity depth table price    x    y    z
## 11417  1.16 Ideal     E     SI2  62.7  56.0  5001 6.69 6.73 4.21
## 11418  1.16 Ideal     E     SI2  59.9  57.0  5001 6.80 6.82 4.08
## 11422  1.07 Ideal     I     SI1  61.7  56.1  5002 6.57 6.59 4.06
## 11423  1.10 Ideal     H     SI2  62.0  56.5  5002 6.58 6.63 4.09
## 11424  1.20 Ideal     J     SI1  62.1  55.0  5002 6.81 6.84 4.24
## 11431  1.14 Ideal     H     SI1  61.6  57.0  5003 6.70 6.75 4.14
## 
## > "Find average price group by color (plyr package is needed)"
## [1] "Find average price group by color (plyr package is needed)"
## 
## > require("plyr")
## Loading required package: plyr
## 
## > ddply(subset(diamonds, cut == "Ideal" & price > 5000), 
## +     ~color, summarise, o = mean(price, na.rm = TRUE))
##   color        o
## 1     D 9056.612
## 2     E 9065.486
## 3     F 9704.489
## 4     G 9392.281
## 5     H 8923.306
## 6     I 9663.031
## 7     J 9406.772

For more on subsetting dataframes see http://www.ats.ucla.edu/stat/r/faq/subset_R.htm

02 RESTful Data Access

source("../02 RESTful Data Access/Access Oracle Database.R", echo = TRUE)
## 
## > require("jsonlite")
## Loading required package: jsonlite
## 
## Attaching package: 'jsonlite'
## 
## The following object is masked from 'package:utils':
## 
##     View
## 
## > require("RCurl")
## Loading required package: RCurl
## Loading required package: bitops
## 
## > df <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from emp order by job\""), 
## +     httpheader = c(DB =  .... [TRUNCATED] 
## 
## > df
##    EMPNO  ENAME       JOB  MGR            HIREDATE  SAL COMM DEPTNO
## 1   7788  SCOTT   ANALYST 7566 1982-12-09 00:00:00 3000 null     20
## 2   7902   FORD   ANALYST 7566 1981-12-03 00:00:00 3000 null     20
## 3   7934 MILLER     CLERK 7782 1982-01-23 00:00:00 1300 null     50
## 4   7900  JAMES     CLERK 7698 1981-12-03 00:00:00  950 null     30
## 5   7369  SMITH     CLERK 7902 1980-12-17 00:00:00  800 null     20
## 6   7876  ADAMS     CLERK 7788 1983-01-12 00:00:00 1100 null     20
## 7   7698  BLAKE   MANAGER 7839 1981-05-01 00:00:00 2850 null     30
## 8   7566  JONES   MANAGER 7839 1981-04-02 00:00:00 2975 null     20
## 9   7782  CLARK   MANAGER 7839 1981-06-09 00:00:00 2450 null     10
## 10  7839   KING PRESIDENT null 1981-11-17 00:00:00 5000 null     10
## 11  7844 TURNER  SALESMAN 7698 1981-09-08 00:00:00 1500 null     30
## 12  7654 MARTIN  SALESMAN 7698 1981-09-28 00:00:00 1250 1400     30
## 13  7521   WARD  SALESMAN 7698 1981-02-22 00:00:00 1250  500     30
## 14  7499  ALLEN  SALESMAN 7698 1981-02-20 00:00:00 1600  300     30

03 Grammar of Graphics with R & ggplot2

ggplot is an R package for data exploration and visualization. It produces production quality graphics and allows you to slice and dice your data in many different ways. ggplot uses a general scheme for data visualization which breaks graphs up into semantic components such as scales and layers. In contrast to other graphics packages, ggplot2 allows the user to add, remove or alter components in a plot at a high level of abstraction.

See also http://ggplot2.org/, http://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf, and https://groups.google.com/forum/#!forum/ggplot2

Grammar of Graphics

plot ::= coord scale+ facet? label? theme? layer+
layer ::= data mapping stat geom position?

source("../03 Grammar of Graphics with R & ggplot2/Plots.R", echo = TRUE)
## 
## > options(java.parameters = "-Xmx2g")
## 
## > head(diamonds)
##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48
## 
## > ggplot(data = diamonds) + geom_histogram(aes(x = carat))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

## 
## > ggplot(data = diamonds) + geom_density(aes(x = carat, 
## +     fill = "gray50"))

## 
## > ggplot(diamonds, aes(x = carat, y = price)) + geom_point()

## 
## > p <- ggplot(diamonds, aes(x = carat, y = price)) + 
## +     geom_point(aes(color = color))
## 
## > p + facet_wrap(~color)

## 
## > p + facet_grid(cut ~ clarity)

## 
## > p <- ggplot(diamonds, aes(x = carat)) + geom_histogram(aes(color = color), 
## +     binwidth = max(diamonds$carat)/30)
## 
## > p + facet_wrap(~color)

## 
## > p + facet_grid(cut ~ clarity)

The Chapter 7 of “R for Everyone” has many more examples of ggplots.

ggplot2 and functions

# source("../03 ggplot/plotFunction.R", echo = TRUE)

You should now be able to open RWorkshop/00 Doc/4diamonds.png. It should look like the following plot.

KnitR

KnitR is an R package designed to generate dynamic reports using a mix of the R, LaTex, and the Rmarkdown (see http://rmarkdown.rstudio.com/?version=0.98.945&mode=desktop) languages.

See also http://yihui.name/knitr/ and http://kbroman.github.io/knitr_knutshell/

Simple examples can be found in “04 KnitR/doc1.Rmd” and “04 KnitR/doc2.Rmd”. These can generate html, pdf, and word documents. The output from Kniting doc2.Rmd is,

A comprehensive KnitR example (which generated this document) can be found in “00 Doc/RWorkshop.Rmd”.

slidify

You can use Slidify to generate HTML slide decks using only the Rmarkdown language.

See also http://slidify.org and http://slidify.org/start.html

Follow the instructions in “05 Slidify/slidify setup.R” to install and run slidify. You should be able to produce a slide deck with a first slide that looks something like the following.

Cool trick - Any github repo with a branch called gh-pages will get served as a website. If the content of that repo is the stuff of websites (html,css), then you get free web hosting. So, create a branch called gh-pages and push to it.

shiny

The shiny R package allows you to build interactive web-based applications using only R with no knowledge of html, css, or javascript needed. You just need to write two scripts (see the example files in the 06Shiny directory):

  • ui.R : Defines the layout and the interactive elements that the user can access.
  • server.R : Defines what computations are done in response to user interactions.

See also http://shiny.rstudio.com and http://shiny.rstudio.com/tutorial

To run the shiny app that’s in the 06Shiny directory run the following in the main RWorkshop directory (make sure the working directory is set to this directory):
library(shiny)
runApp(“06Shiny”) # Make sure there are no spaces in the string argument to runAPP

This should pop the application up in a browser, you can also access it in a browser at http://127.0.0.1:6837. It should look like the following.

shinyapps

The example above ran the shiny app on your local machine, but to share with others, you have to send around the R files and the user needs to have R and know a little bit about it.

Instead, you can remotely host shiny apps and then just send people links. Get a free account at shinyapps.io/signup.html and give it a try.

library(“devtools”, lib.loc=“/Library/Frameworks/R.framework/Versions/3.0/Resources/library”)
install_github( repo = “shinyapps”, username=“rstudio” )
shinyapps::setAccountInfo(name=‘pcannata’, token=‘3ECF447A741004F6A8B7208C9ED778E1’, secret=‘. . .’)

# library(shinyapps)
getwd()
## [1] "/Users/pcannata/Mine/UT/GitRepositories/DataVisualization/RWorkshop/00 Doc"
# Uncomment the following line to deploy the app.
#deployApp("../06Shiny")

Now you can try the app at https://pcannata.shinyapps.io/06Shiny/

See also https://www.shinyapps.io/ and http://shiny.rstudio.com/articles/shinyapps.html

Type of RestfulReL Oracle Cloud Connections

# source("../08 eval(parse vs. json/ParseEval vs JSON.R", echo = TRUE)

Joining Data

# source("../09 Joining Data/Joining Data.R", echo = TRUE)

http://www.rstudio.com/resources/cheatsheets/

Lists Indexing

# source("../10 ListsForIfFunctionsPng/List Indexing.R", echo = TRUE)

For more details on [[…]], see http://stackoverflow.com/questions/1169456/in-r-what-is-the-difference-between-the-and-notations-for-accessing-the

Lists, For and If Statements, Functions, and generating png Files

# source("../10 ListsForIfFunctionsPng/ListsForIfFunctionsPng.R", echo = TRUE)

Bokey

Based upon http://hafen.github.io/rbokeh/

# source("../12 Bokeh/Bokeh.R", echo = TRUE)